Start Updating the partial singular value decomposition in latent semantic indexing

Updating the partial singular value decomposition in latent semantic indexing

In a rapidly expanding environment, a term-document matrix is altered often as new documents and terms are added.

Furthermore, we developed a new LSI technique, one which replaces the Singular Value Decomposition (SVD) with another technique for matrix factorization, the sparse column-row approximation (SCRA).

We were able to conclude that all three LSI techniques have similar performance.

The PSVD-updating method is computationally more expensive than the folding-inmethod, but better maintains the accuracy of the PSVD.